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	SegRNN: Segment Recurrent Neural Network för långsiktig tidsserieprognostisering	Gated Recurrent Unit (GRU)
Ämnesområde	Djupinlärning	Djupinlärning
Familj	Machine learning	Machine learning
Ursprungsår	2023	2014
Upphovsperson	Shengsheng Lin et al.	Cho, K. et al.
Typ	Segment-based recurrent forecasting model	Gated recurrent neural network unit
Ursprungskälla	Lin, S., Lin, W., Wu, W., Zhao, F., Mo, R., & Zhang, H. (2023). SegRNN: Segment recurrent neural network for long-term time series forecasting. arXiv preprint.	Cho, K. et al. (2014). Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation. EMNLP.
Alias	Segment RNN, Segment Recurrent Neural Network, SegRNN forecaster, Bölümlü Tekrarlayan Sinir Ağı	Kapılı Tekrarlayan Birim (GRU), gated recurrent unit, gated recurrent network
Närliggande	3	5
Sammanfattning	SegRNN is a recurrent neural network architecture for long-term time series forecasting proposed by Shengsheng Lin et al. in 2023. Instead of processing one time step at a time, SegRNN partitions input sequences into fixed-length segments and feeds each segment as a single token into a GRU. This segment-based design drastically reduces the number of recurrent iterations, addressing the well-known difficulty RNNs face when modeling very long dependencies over many individual steps.	The Gated Recurrent Unit (GRU) is a gated recurrent neural network cell introduced by Cho and colleagues in 2014 that captures long-range dependencies in sequential data using update and reset gates, achieving performance comparable to LSTM with fewer parameters.